<![CDATA[The development of e-government has encouraged every Regional Apparatus Organization (OPD) within the Padang City Government to submit various digital applications to improve the quality of public services. However, the large number of applications often creates challenges in determining priorities, primarily due to limited resources and budgets. This research aims to design a Hybrid Decision Support System (DSS) that combines the WASPAS (Weighted Aggregated Sum Product Assessment) method and the development of the K-Means Clustering method to provide a more objective and measurable priority classification. The WASPAS method is used to provide a ranking of alternatives based on predetermined criteria, such as urgency of need, service impact, funding availability, and alignment with the regional strategic plan. Next, the K-Means algorithm is applied to group the calculation results into several priority classes, ranging from the most urgent to the least urgent. As an innovation, this research also utilizes image processing techniques to visualize the K-Means classification results, allowing for a more intuitive and easily understood presentation of priority grouping patterns for decision-makers. In this research, data were collected from 52 OPDs within the Padang City Government as a case study. The test results show that the hybrid DSS approach combining WASPAS and K-Means successfully produces priority scale classification with an accuracy level of 94.75%, which demonstrates consistency and accelerates the application evaluation process at OPDs. Integration with image processing for visualization of clustering results also successfully helps clarify data interpretation and facilitates analysis. Thus, this system is expected to support more effective, transparent decision-making in accordance with the principles of electronic-based governance in Padang City.]]>
